开发者工具4 days ago
AI project management for developers | Ravel
Ravel：为开发者提供AI驱动的项目管理，支持GitHub跟踪和冲刺规划。
@Gallagh819 · X
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Ravel：为开发者提供AI驱动的项目管理，支持GitHub跟踪和冲刺规划。
@Gallagh819 · X
Flowdia 用 AI 团队来规划、设计和部署配备真实数据库和 API 的应用。
@islamtaha · X
用 Google Gemini 生成和编辑 AI 图像
Nano Banana AI — 基于市面上最强的 AI 图像编辑模型 Nano Banana 开发的图片编辑器，用嘴改图、保持角色一致性、控制细节、图片融合
自动生成网站地图、Schema标记、元标签和robots.txt文件的AI工具。
@PeakVisibilitys · X
🚀 AI-powered SEO audits and AI search optimization. Would love some feedback!
存储提示词和背景信息，通过 MCP 让 Claude Code、Cursor 等 AI 工具共享访问。
@vibexp_io · X
Your plan is now code. Claude Code Dynamic Workflows fan out up to 1,000 subagents, 16 at once, each in its own context, verifying until the results converge. Built for big bug hunts, migrations and audits. Source:
与航空飞行数据交互，创建自动告警的AI监测智能体。
@yenalmh · X
使用 FRAI 扫描网站中的 AI 使用情况，并测试聊天机器人的偏差和安全性。
@sebuzdugan · X
building @getfrai , an open source toolkit that helps ML engineers navigate EU AI Act compliance, model cards, risk files, the boring but necessary stuff
诊断作物病害，获取天气建议、农产品价格和政府补助信息。
@deep_pakhare · X
🚀 Submitted my capstone project for Kaggle's AI Agents: Intensive Vibe Coding Course with Google Introducing FarmGPT AI 🌾 🔗 GitHub: 🌐 Demo: 📖 Write-up: #AI #Kaggle #GoogleAI #React #BuildInPublic
AI workspace for teams to automate workflows, chat with an assistant, analyze data, and manage projects.
@HonEmmi · X
I'm working on Neuralflow Ai It acts as an AI workspace where teams can automate workflows, chat with an AI assistant, analyze business data, summarize documents, manage projects, and collaborate from one central platform.
编写目标，AI代理（Claude、Cursor、Codex）分解并执行编程任务。
dudemanAtl · HN
PlanWright – A control plane for AI coding agents
Flint是为AI代理设计的可视化语言,用于创建交互式数据可视化。
chenglong-hn · HN
Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple
